An integrated linear technique for Pose estimation from different geometric features

Existing linear solutions for the pose estimation (or exterior orientation) problem suffer from a lack of robustness and accuracy partially due to the fact that the majority of the methods utilize only one type of geometric e ntity and their frameworks do not allow simultaneous use of different types of features. Furthermore, the orthonormality constraints are weakly enforc ed or not enforced at all. We have developed a new analytic linear least-sq uares framework for determining pose from multiple types of geometric featu res. The technique utilizes correspondences between points, between lines a nd between ellipse-circle pairs. The redundancy provided by different geome tric features improves the robustness and accuracy of the least-squares sol ution. A novel way of approximately imposing orthonormality constraints on the sought rotation matrix within the linear framework is presented. Result s from experimental evaluation of the new technique using both synthetic da ta and real images reveal its improved robustness and accuracy over existin g direct methods.